CN109667727B - Wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis - Google Patents
Wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis Download PDFInfo
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Abstract
The invention discloses a wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis. The method is based on real-time operation data of a wind turbine generator data acquisition and monitoring control (SCADA) system including wind speed, active power, yaw error, environment temperature, environment air pressure and the like, firstly, the data are preprocessed to a certain degree, then, the wind speed and the power data are divided according to a certain yaw error interval, power curves under different yaw error intervals are respectively fitted through a standard power curve fitting process, further, different power curves are subjected to quantitative analysis, an interval range of yaw error inherent deviation values is determined based on an interval judgment criterion, and finally, the identified inherent deviation values are directly compensated to actual yaw error measurement values in an incremental mode. The method is based on data driving, has no special requirements on the operation data of the wind turbine generator, has strong universality and has strong application value on the performance improvement of the wind turbine generator.
Description
Technical Field
The invention relates to a method for identifying and compensating the inherent deviation of a yaw error of a wind turbine generator, in particular to a method for identifying and compensating the inherent deviation of the yaw error of the wind turbine generator based on power curve analysis.
Background
In the modern society with the shortage of traditional fossil energy resources and serious pollution, wind energy is widely popular to the public as a new pollution-free and renewable energy, and the wind power industry is one of novel renewable energy industries which are vigorously developed at home and abroad. In China, the construction and related research work of wind power plants are remarkably improved in quantity and quality in the last decade, but a series of negative factors caused by the continuous degradation of wind generating sets are accompanied while the wind power generation industry is vigorously developed. In the use process of the existing wind turbine generator, because the wind speed has the characteristics of intermittency and uncertainty of height, the performance evaluation of the wind turbine generator is greatly influenced, the performance condition of the wind turbine generator is accurately evaluated, and effective means for improving the performance of the wind turbine generator are the important point for improving the competitiveness of wind power in new energy power generation.
At present, when a wind power generation system is used for dealing with wind direction changes, the maximum wind energy capture efficiency is obtained by adjusting a yaw system. Fig. 2 is a schematic view of a yaw control strategy of a wind turbine generator, where a specific control strategy of a yaw system and an actuator is to ensure that a yaw error value is as small as possible, and a physical meaning in practice is to control a swept surface of a blade of the wind turbine generator to face an incoming wind direction as much as possible, that is, to control an angle of a yaw error θ to be as close to 0 ° as possible. In the related application of the current wind power industry, the wind turbine generator adopts a direct measurement mode for determining the yaw error angle: namely, a wind direction indicator is arranged behind the cabin and the position of a zero reticle of the wind direction indicator is calibrated to be parallel to the direction of the cabin; under the normal operation condition of the wind turbine generator, the sensor feeds a measured wind direction value back to the yaw system, and the yaw system controls the cabin to adjust the direction of the incoming wind based on a yaw control strategy of the yaw system. However, the anemoscope mainly has the following two problems during actual installation and operation and maintenance:
(1) the installation worker usually does not need to use measuring equipment, but only uses experience or visual measurement to calibrate the zero reticle position of the anemoscope;
(2) during repeated rotation of the wind vane during actual operation, return errors can also occur for mechanical reasons.
The gradual accumulation of the two aspects tends to bring large errors to the measurement of the yaw error angle, thereby affecting the performance of the yaw system. Therefore, under the background that the related research of the intelligent identification and compensation technology based on data analysis in the field of performance improvement of the wind turbine generator is still in the technical gap, based on the research idea of controller improvement, the inherent deviation value of the yaw error needs to be identified and fed back to the yaw system to compensate for the problem of zero position error of the wind turbine generator cabin anemoscope, so that the purpose of improving the output performance of the wind power generation system is very significant.
Disclosure of Invention
The invention aims to fill the technical blank of the intelligent identification and compensation technology based on data analysis in the field of performance improvement of wind turbines, and provides a method for identifying and compensating the inherent deviation of the yaw error of a wind turbine based on power curve analysis. The method is based on data analysis, real power curves of the wind turbine generator under different yaw error intervals are fitted, corresponding indexes are designed for performance quantification, and finally, the identification and compensation of the yaw error inherent deviation are achieved by combining a simple and effective yaw error inherent deviation identification criterion and a yaw error inherent deviation compensation strategy, so that the method has high practical application value for improving the power generation output performance of the wind turbine generator.
The purpose of the invention is realized by the following technical scheme: a wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis comprises the following steps:
1) reading wind turbine generator operation data information measured in an SCADA system of N wind turbine generators to be analyzed in corresponding demand periods according to the wind turbine generator yaw error inherent deviation identification and compensation demands, wherein the information comprises wind speed { v } viActive power { P }i}, ambient air pressure { Bi}, ambient temperature { TiAnd yaw error thetaiRecording the information data set asWherein i is 1, 2, 3, …, N;
2) based on the information data set in step 1)Calculating to obtain the air density [ rho ] of the corresponding momentiAnd wind speed { v } in the information data setiCorrection of the information to a reference air density ρ0Corrected wind speed ofWherein i is 1, 2, 3, …, N;
3) correcting the wind speed in the step 2)Active power { PiAnd yaw error thetaiSynthesizing into a wind turbine yaw error inherent deviation analysis data set { X }iAnd will { X at a certain yaw error intervaliDivide it into M intervals, note the data number in the k yaw error division interval is NkThe yaw error inherent bias analysis data set isWherein k is 1, 2, 3, …, M, l is 1, 2, 3, …, Nk;
4) Yaw error inherent deviation analysis data set based on M intervalsRespectively fitting M real power curves, and recording the real power curve under the k-th yaw error interval as { PCkWhere k is 1, 2, 3, …, M;
5) respectively calculating real Power Curves (PC) under M yaw error intervalskRespective quantization performance index PIkWherein k is 1, 2, 3, …, M;
6) determining the yaw error inherent deviation value theta of the wind turbine generator set through the yaw error inherent deviation identification criterionimAnd identifying the deviation value thetaimCompensating to the actual yaw error measurement value theta directly in an incremental manner to obtain a final compensated yaw error true value theta';
the yaw error inherent deviation identification criterion is defined as follows: real Power Curve (PC) under all M yaw error intervals in the step 5)kQuantized Performance indicator PIkArranged from big to small and selects the maximum quantization performance index PImaxCorrespond toThe index k' of the interval, the yaw error intrinsic bias value thetaimThe identification result calculation formula is as follows
Wherein theta islbAnd thetaubRespectively a lower bound and an upper bound of the yaw error range to be analyzed.
As a further elaboration, in step 2) of the method, the density ρ of the airiAnd correcting wind speedThe calculation formula of (a) is as follows:
2-a) air density ρi:
Wherein R is0Is the specific gas constant of the drying air; rwIs the specific gas constant of water vapor; pwIs the pressure of water vapor; b isiFor ambient air pressure, obtained by SCADA system, or byEstimate of where B0The standard sea level atmospheric pressure is adopted, e is a natural constant, g is gravity acceleration, z is the altitude at the hub of the wind turbine generator, and R is a specific gas constant of air;for relative ambient humidity, obtained or set by SCADA system
Where ρ is0Is referred to as air density.
As a further elaboration, in step 3) of the method, the yaw error intrinsic bias analysis dataset { XiThe method for dividing the intervals comprises the following steps:
3-a) plotting yaw error θiThe frequency distribution histogram of the yaw error range to be analyzed is set, and the lower limit theta of the yaw error range to be analyzed is set based on the distribution condition of the frequency distribution histogramlbAnd an upper bound θub;
3-b) set yaw error intrinsic bias analysis dataset { XiDividing the interval of the M points into M number;
3-c) ofPartitioning intervals for yaw error intervals, analyzing data set { X) for yaw error inherent biasiDivide.
As a further description, in step 4), the flow of acquiring the real power curve of the wind turbine generator under the M yaw error intervals is as follows:
4-a) setting a real power curve to obtain an initial interval k equal to 1;
4-b) determining yaw error intrinsic bias analysis data set under k yaw error intervalCorrected wind speed inCorresponding maximum valueNote the bookWherein v iscut_offCutting out wind speed for the wind turbine generator;
4-c) interval at fixed wind speedΔ v is a wind speed interval division interval, and a yaw error inherent deviation analysis data set under the k-th yaw error intervalFurther based on corrected wind speedDividing the data into a yaw error inherent deviation analysis data set in the jth corrected wind speed intervalIs defined as
k=1,2,3,…,M j=1,2,3,…,Mkm=1,2,3,…,Mk,j
Wherein M isk,jAnalyzing the data set for the yaw error inherent deviation in the jth corrected wind speed intervalThe number of data in (1); mkAnalyzing the data set for the yaw error inherent deviation under the k yaw error intervalThe number of the corrected wind speed interval divisions is calculated as follows
4-d) calculating the yaw error inherent bias analysis dataset for each corrected wind speed intervalAverage corrected wind speed ofAnd average active powerThe formula is as follows
4-e) average corrected wind speed for each corrected wind speed intervalAnd average active powerCarrying out maximum value-minimum value normalization processing to obtain normalized average corrected wind speedAnd average active power
4-f) correcting the wind speed based on the averageAnd average active powerDetermining the power curve fitting central point under each corrected wind speed intervalThe determination is as follows: analyzing the data set of the yaw error inherent deviation in the jth corrected wind speed intervalNumber of data in (M)k,jIf the power curve is equal to 0, the power curve is not fitted to the central point in the interval; otherwise, the power curve fitting center point in the interval is considered
4-g) supplementary definition of center pointAnd recording the number of the fitting center points of the power curve in the kth yaw error interval as M'kCalculating the fitted center point of each power curveCorresponding parameter valueIs given by the formula
WhereinFitting center points for two adjacent power curvesAndcorresponding to the chord length after coordinate normalization, i.e.
dkFitting all power curves with the total chord length normalized by the coordinates corresponding to the center point, i.e.
4-h) fitting the power curve in the kth yaw error interval by adopting a least square B spline fitting algorithm, wherein a fitting function B is obtainedk(t) is defined as follows:
wherein N isn,p(t) is a standard function of the nth segment of B spline fitting function with the order p, t is an independent variable of the least square B spline fitting function,fitting the nth control point of the function to the least squares B-spline;for a segment node, i ═ 0, 1, 2, …, p-1, p, p +1, …, M'k-1,M′k,M′k+1,…,M′k+ p, the calculation is as follows:
4-i) determining a B-spline fitting function B based on a least squares optimization functionkAll control points in (t)
4-j) solving the least square B-spline fitting function Bk(t) as a result of the true power curve in the kth yaw error interval { PC) converted to a polynomial form with the independent variable as the wind speed vk};
4-k) setting an interval k to be analyzed as k +1, and repeating the steps 4-b) to 4-j) until j is larger than M.
As a further description, the method stepsIn step 5), the performance index PI is quantifiedkIs defined as follows:
wherein N ishIs a number of 1 year time to hour; CAP is rated power of the wind turbine generator to be analyzed;is the median value of the wind speed in the jth corrected wind speed interval under the kth yaw error interval, namely True power curve for k-th yaw error interval PCkOn (c)Corresponding active power value, andthe F (-) function is a cumulative probability distribution function of Rayleigh distribution, and the specific formula is as follows
Wherein v isaveThe annual average wind speed of the wind turbine generator to be analyzed.
Compared with the prior art, the invention has the following innovative advantages and remarkable effects:
1) the method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on the power curve analysis is innovatively provided, and the technical blank of the intelligent identification and compensation technology based on the data analysis in the field of performance improvement of the wind turbine generator is filled.
2) The method designs a wind turbine performance improving flow including the steps of wind turbine real power curve fitting, power curve energy index construction, yaw error inherent deviation identification, yaw error inherent deviation compensation strategy formulation and the like, and has strong practicability, reliability and expansibility.
Drawings
FIG. 1 is a flow chart of a method for identifying and compensating the inherent deviation of the yaw error of the wind turbine generator based on power curve analysis according to the invention;
FIG. 2 is a schematic diagram illustrating an angular correlation relationship such as an inherent yaw error deviation of a wind turbine generator in the field of application of the present invention;
FIG. 3 is a scatter diagram of raw data of a power curve of a wind turbine in step 1) according to an embodiment of the present invention;
FIG. 4 is a histogram of yaw error frequency distribution before compensation in step 3) when the present invention is applied to an embodiment;
FIG. 5 is a graph showing the results of correlation of power curves of the yaw error interval [ -1 °, 0 ° ] in steps 3) and 4) when the present invention is applied to the examples;
FIG. 6 is a graph showing the result of the power curve quantization performance index in each yaw error interval before the yaw error inherent offset compensation in step 5) when the present invention is applied to the embodiment;
fig. 7 is a diagram showing a result of a power curve quantization performance index in each yaw error interval after the yaw error inherent offset compensation in step 6) when the present invention is applied to the embodiment.
Detailed Description
The following detailed description of the embodiments and the working principles of the present invention is made with reference to the accompanying drawings:
examples
Because the wind conditions of the wind turbines in the wind power plant are difficult to be completely consistent when the wind turbines operate in different time periods, for the verification of the effectiveness of the method, the data adopted in the embodiment is simulation data of GH blade 3.82 under the same type of wind turbine and the same wind file to analyze and research the inherent deviation identification and compensation method of the yaw error of the wind turbine. The data sampling interval of the wind turbine generator is 10min, the data information is 5 years in period, and 284405 pieces are in total. The specific variables and related data information included in the data set are shown in tables 1 and 2:
simulation data set variable information of wind turbine generator of certain model under Table 1 GH Bladed 3.82
Variable names | Meaning of variables | Variable unit |
Wind speed v | Current wind turbine generator system cabin wind speed | m/s |
Active power P | Active power of current wind turbine generator | kW |
Ambient temperature T | Operating environment temperature of wind turbine generator | ℃ |
Ambient air pressure B | Wind turbine generator system operating environment air pressure | Pa |
Yaw error theta | Yaw error of current wind turbine generator | ° |
Part simulation data of certain type of wind turbine generator under certain wind file under Table 2 GH Bladed 3.82
Data sequence number | Wind speed | Active power | Ambient temperature | Ambient air pressure | Yaw error |
… | … | … | … | … | … |
105679 | 4.2992 | 81.0290 | 25.0000 | 100463.2887 | 5.7852 |
105680 | 4.5417 | 81.8810 | 25.0000 | 100463.2887 | 15.2980 |
105681 | 4.9667 | 82.8700 | 25.0000 | 100463.2887 | 1.6641 |
… | … | … | … | … | … |
235640 | 11.6990 | 1504.7000 | 25.0000 | 100463.2887 | 6.0619 |
235641 | 11.5200 | 1549.5000 | 25.0000 | 100463.2887 | 9.1317 |
235642 | 11.1470 | 1550.0000 | 25.0000 | 100463.2887 | -0.0520 |
… | … | … | … | … | … |
It is worth mentioning that the yaw error measured in the GH Bladed does not have the inherent deviation of the yaw error existing in the measurement process of the anemoscope in the actual application process, so that the phenomenon that the inherent deviation of the yaw error of minus 5 degrees exists in the actual process is simulated by adopting a mode of artificially changing the measured value to plus 5 degrees in the simulation process. In this embodiment, the implementation of the yaw error inherent deviation identification and compensation method is performed by using all the simulation data by default, the method result is the obtained identification result of the yaw error inherent deviation of the wind turbine generator system, and the verification of the method effectiveness is performed by a compensation means, and the detailed implementation steps are as follows:
1) reading wind turbine generator operation data information measured in an SCADA system of N wind turbine generators to be analyzed in corresponding demand periods according to the wind turbine generator yaw error inherent deviation identification and compensation demands, wherein the information comprises wind speed { v } viActive power { P }i}, ambient air pressure { Bi}, ambient temperature { TiAnd yaw error thetaiRecording the information data set asWherein i is 1, 2, 3, …, N; according to the description of the variable information of the data set listed in tables 1 and 2, the data set in this embodiment includes all necessary information in this step, and the result shown in fig. 3 is a raw data scatter diagram of the power curve of the wind turbine generator system in this step;
2) based on the information data set in step 1)Calculating to obtain the air density [ rho ] of the corresponding momenti} andwind speed { v) in a data set of informationiCorrection of the information to a reference air density ρ0Corrected wind speed ofWherein i is 1, 2, 3, …, N; the correlation calculation formula is as follows:
2-a) air density ρi:
Wherein R is0Is the specific gas constant of the drying air; rwIs the specific gas constant of water vapor; pwIs the pressure of water vapor; b isiFor ambient air pressure, obtained by SCADA system, or byEstimate of where B0The standard sea level atmospheric pressure is adopted, e is a natural constant, g is gravity acceleration, z is the altitude at the hub of the wind turbine generator, and R is a specific gas constant of air;for relative ambient humidity, obtained or set by SCADA systemIn this example, the ambient temperature and the ambient air pressure were fixed at 25 ℃ and 100463.2887Pa, respectively, so that the air density calculation result was 1.1738kg/m3。
Where ρ is0For reference to the air density, 1.225kg/m is taken in this example3。
3) Correcting the wind speed in the step 2)Active power { PiAnd yaw error thetaiSynthesizing into a wind turbine yaw error inherent deviation analysis data set { X }iAnd will { X at a certain yaw error intervaliDivide it into M intervals, note the data number in the k yaw error division interval is NkThe yaw error inherent bias analysis data set isWherein k is 1, 2, 3, …, M, l is 1, 2, 3, …, Nk(ii) a One preferred method of using the yaw error interval division is as follows, but is not limited thereto:
3-a) plotting yaw error θiThe frequency distribution histogram of the yaw error range to be analyzed is set, and the lower limit theta of the yaw error range to be analyzed is set based on the distribution condition of the frequency distribution histogramlbAnd an upper bound θub;
3-b) set yaw error intrinsic bias analysis dataset { XiDividing the interval of the M points into M number;
3-c) ofPartitioning intervals for yaw error intervals, analyzing data set { X) for yaw error inherent biasiDividing, and analyzing the yaw error inherent deviation analysis data set of the k-th yaw error intervalIs defined as
k=1,2,3,…,M l=1,2,3,…,Nk
Wherein N iskAnalyzing the data set for the yaw error inherent deviation under the k yaw error intervalThe number of data in (1). In the present embodiment, only the corresponding yaw error interval is given as [ -1 °, 0 ° ] for space limitation reasons]The power curve scatter plot of (a) is shown in fig. 5.
4) Yaw error inherent deviation analysis data set based on M intervalsRespectively fitting M real power curves, and recording the real power curve under the k-th yaw error interval as { PCkWhere k is 1, 2, 3, …, M; one preferred algorithm flow for the true power curve acquisition employed is as follows, but is not limited thereto:
4-a) setting a real power curve to obtain an initial interval k equal to 1;
4-b) determining yaw error intrinsic bias analysis data set under k yaw error intervalCorrected wind speed inCorresponding maximum valueNote the bookWherein v iscut_offCutting out wind speed for the wind turbine generator;
4-c) dividing intervals by taking a fixed wind speed interval delta v as a wind speed interval, and analyzing a data set of yaw error inherent deviation under the k-th yaw error intervalFurther based on corrected wind speedDividing into j th correction windYaw error inherent bias analysis dataset at speed intervalIs defined as
k=1,2,3,…,M j=1,2,3,…,Mkm=1,2,3,…,Mk,j
Wherein M isk,jAnalyzing the data set for the yaw error inherent deviation in the jth corrected wind speed intervalThe number of data in (1); mkAnalyzing the data set for the yaw error inherent deviation under the k yaw error intervalThe number of the corrected wind speed interval divisions is calculated as follows
4-d) calculating the yaw error inherent bias analysis dataset for each corrected wind speed intervalAverage corrected wind speed ofAnd average active powerThe formula is as follows
4-e) average corrected wind speed for each corrected wind speed intervalAnd average active powerCarrying out maximum value-minimum value normalization processing to obtain normalized average corrected wind speedAnd average active power
4-f) correcting the wind speed based on the averageAnd average active powerDetermining the power curve fitting central point under each corrected wind speed intervalThe determination is as follows: analyzing the data set of the yaw error inherent deviation in the jth corrected wind speed intervalNumber of data in (M)k,jIf the power curve is equal to 0, the power curve is not fitted to the central point in the interval; otherwise, the power curve fitting center point in the interval is considered
4-g) supplementary definition of center pointAnd recording the number of the fitting center points of the power curve in the kth yaw error interval as M'kCalculating the fitted center point of each power curveCorresponding parameter valueIs given by the formula
WhereinFitting center points for two adjacent power curvesAndcorresponding to the chord length after coordinate normalization, i.e.
dkFitting all power curves with the total chord length normalized by the coordinates corresponding to the center point, i.e.
4-h) fitting the power curve in the kth yaw error interval by adopting a least square B spline fitting algorithm, wherein a fitting function B is obtainedk(t) is defined as follows:
wherein N isn,p(t) is the nth segment of B spline fitting function with the order of pT is the argument of the least squares B-spline fitting function,fitting the nth control point of the function to the least squares B-spline;for a segment node, i ═ 0, 1, 2, …, p-1, p, p +1, …, M'k-1,M′k,M′k+1,…,M′k+ p, the calculation is as follows:
4-i) determining a B-spline fitting function B based on a least squares optimization functionkAll control points in (t)
4-j) solving the least square B-spline fitting function Bk(t) as a result of the true power curve in the kth yaw error interval { PC) converted to a polynomial form with the independent variable as the wind speed vk};
4-k) setting the interval k to be analyzed as k +1, and repeating the steps 4-b) to 4J) until J > M. Due to space limitations, the calculation process and secondary results of each process parameter are omitted in the fitting of the relevant power curve in the embodiment, and the relevant important parameters take the following values: corrected wind speed corresponds to a maximum value ofThe fixed wind speed interval delta v is 2m/s, and the 14 th yaw error interval is [ -1 DEG, 0 DEG ]]The number of the corrected wind speed intervals in the power curve related data is 15, and the fitting center point and the fitting result of the corresponding real power curve are shown in the figureThe symbol "■" in FIG. 5 and the curve.
5) Respectively calculating real Power Curves (PC) under M yaw error intervalskRespective quantization performance index PIkWherein k is 1, 2, 3, …, M; quantitative performance index PIkIs defined as follows:
wherein N ishIs a number of 1 year time to hour; CAP is rated power of the wind turbine generator to be analyzed;is the median value of the wind speed in the jth corrected wind speed interval under the kth yaw error interval, namelyAnd is True power curve for k-th yaw error interval PCkOn (c)Corresponding active power value, andthe F (-) function is a cumulative probability distribution function of Rayleigh distribution, and the specific formula is as follows
Wherein v isaveThe annual average wind speed of the wind turbine generator to be analyzed. In this embodiment, the relevant important parameters take the following values: n is a radical ofh8760 is calculated according to 365 days in 1 year; CAP is rated power value of the wind turbine generator set, and 1 is taken550kW;vaveTaking the average wind speed of the simulation wind file as 7m/s, corresponding to the respective quantitative performance indexes PI of the real power curves under 20 yaw error intervalskThe calculation results are shown in fig. 6.
6) Determining the yaw error inherent deviation value theta of the wind turbine generator set through the yaw error inherent deviation identification criterionimAnd identifying the deviation value thetaimCompensating the actual measured value theta of the yaw error in an incremental mode directly to obtain the final compensated real value theta' of the yaw error, namely theta ═ theta + thetaim;
The yaw error inherent deviation identification criterion is defined as follows: real Power Curve (PC) under all M yaw error intervals in the step 5)kQuantized Performance indicator PIkArranged from big to small and selects the maximum quantization performance index PImaxCorresponding to the index k' of the interval, the inherent deviation value theta of the yaw errorimThe identification result calculation formula is as follows
Wherein theta islbAnd thetaubRespectively a lower bound and an upper bound of the yaw error range to be analyzed. In this embodiment, the quantization performance index PI of the real power curve in fig. 6kMaximum value of (PI)maxMarked by using the ★ symbol, the serial number of the interval corresponding to the maximum value is 9, i.e. the yaw error interval is [ -6 °, -5 ° ]]Then the yaw error intrinsic bias value theta can be usedimCalculating the identification result of the inherent deviation value of the yaw error to be-5.5 degrees by using the identification result calculation formula; further based on a yaw error inherent deviation compensation strategy, the measured value theta of the yaw error is artificially added by 5.5 degrees to be changed into corrected theta ', namely theta' is equal to theta +5.5 degrees; the corrected result is used as yaw control input to perform data simulation after the wind turbine generator is compensated again under the same wind file, and the respective quantitative performance indexes PI of the real power curves shown in the figure 7 can be obtained through the same analysis processkThe calculation results show that the relevant important parameters are as follows: frequency division of yaw errorThe 10% and 90% quantiles of the cloth histogram are-9.681 ° and 10.498 °, respectively, i.e., the lower bound of yaw error θlbAnd an upper bound θubRespectively at-10 ° and 10 °; taking 20 as the number M of interval division; corrected wind speed corresponds to a maximum value ofThe fixed wind speed interval delta v is 2m/s, and other key parameters are the same as the parameters before compensation. As can be seen from fig. 7, after the identification and compensation of the inherent deviation of the yaw error, the serial number of the section corresponding to the maximum value is 10, that is, the identification section of the inherent deviation of the yaw error after the compensation is [0 °, 1 ° ]]Therefore, the compensation of the inherent deviation improves the yaw control effect under the existence of the inherent deviation, and the result value of the power curve quantization index can also show that the power curve quantization index result under the same yaw position is also improved by 20-30 h, and compared with the performance before the compensation, the performance is improved by about 0.8-1.2%. Therefore, the effectiveness and the practicability of the wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis are successfully verified on a simulation data set of GH Bladed 3.82 simulation software.
The invention relates to a method for identifying and compensating the inherent deviation of a yaw error of a wind turbine generator based on power curve analysis, which mainly comprises links such as wind speed correction based on air density, division of a yaw error interval, real power curve fitting of the wind turbine generator, power curve quantitative index calculation, identification and compensation of the inherent deviation of the yaw error and the like. FIG. 1 is a specific flow of real-time and application of a wind turbine yaw error inherent deviation identification and compensation method based on power curve analysis. According to the whole embodiment, analysis is carried out based on SCADA data of the wind turbine generator according to the process shown in FIG. 1, and the performance improvement requirement of the wind turbine generator is realized by fitting the real power curve of the wind turbine generator under different yaw error intervals and finally based on the identification criterion and the compensation strategy of the inherent deviation of the yaw error. Fig. 2 to 7 show results of each link in the flow of identifying and compensating the yaw error inherent deviation of the wind turbine generator by using the method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on power curve analysis according to the present invention, which has strong application value and significance for enterprises with requirements for performance improvement of the wind turbine generator.
Claims (4)
1. A wind turbine generator yaw error inherent deviation identification and compensation method based on power curve analysis is characterized by comprising the following steps:
1) reading wind turbine generator operation data information measured in an SCADA system of N wind turbine generators to be analyzed in corresponding demand periods according to the wind turbine generator yaw error inherent deviation identification and compensation demands, wherein the information comprises wind speed { v } viActive power { P }i}, ambient air pressure { Bi}, ambient temperature { TiAnd yaw error thetaiRecording the information data set asWherein i is 1, 2, 3, …, N;
2) based on the information data set in step 1)Calculating to obtain the air density [ rho ] of the corresponding momentiAnd wind speed { v } in the information data setiCorrection of the information to a reference air density ρ0Corrected wind speed ofWherein i is 1, 2, 3, …, N;
3) correcting the wind speed in the step 2)Active power { PiAnd yaw error thetaiSynthesizing into a wind turbine yaw error inherent deviation analysis data set { X }iAnd will { X at a certain yaw error intervaliDivide it into M intervals, note the data number in the k yaw error division interval is NkThe yaw error inherent bias analysis data set isWherein k is 1, 2, 3, …, M, l is 1, 2, 3, …, Nk;
4) Yaw error inherent deviation analysis data set based on M intervalsRespectively fitting M real power curves, and recording the real power curve under the k-th yaw error interval as { PCkWhere k is 1, 2, 3, …, M;
5) respectively calculating real Power Curves (PC) under M yaw error intervalskRespective quantization performance index PIkWherein k is 1, 2, 3, …, M; quantitative performance index PIkIs defined as follows:
wherein N ishIs a number of 1 year time to hour; CAP is rated power of the wind turbine generator to be analyzed;is the median value of the wind speed in the jth corrected wind speed interval under the kth yaw error interval, namelyAnd is True power curve for k-th yaw error interval PCkOn (c)Corresponding active power value, andthe F (-) function is a cumulative probability distribution function of Rayleigh distribution, and the specific formula is as follows
Wherein v isaveThe annual average wind speed of the wind turbine generator to be analyzed;
6) determining the yaw error inherent deviation value theta of the wind turbine generator set through the yaw error inherent deviation identification criterionimAnd identifying the deviation value thetaimCompensating to the actual yaw error measurement value theta directly in an incremental manner to obtain a final compensated yaw error true value theta';
the yaw error inherent deviation identification criterion is defined as follows: real Power Curve (PC) under all M yaw error intervals in the step 5)kQuantized Performance indicator PIkArranged from big to small and selects the maximum quantization performance index PImaxCorresponding to the index k' of the interval, the inherent deviation value theta of the yaw errorimThe identification result calculation formula is as follows
Wherein theta islbAnd thetaubRespectively a lower bound and an upper bound of the yaw error range to be analyzed.
2. The method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on the power curve analysis as claimed in claim 1, wherein in the step 2), the air density p isiAnd correcting wind speedThe calculation formula of (a) is as follows:
2-a) air density ρi:
Wherein R is0Is the specific gas constant of the drying air; rwIs the specific gas constant of water vapor; pwIs the pressure of water vapor; b isiFor ambient air pressure, obtained by SCADA system, or byEstimate of where B0The standard sea level atmospheric pressure is adopted, e is a natural constant, g is gravity acceleration, z is the altitude at the hub of the wind turbine generator, and R is a specific gas constant of air;for relative ambient humidity, obtained or set by SCADA system
Where ρ is0Is referred to as air density.
3. The method for identifying and compensating the yaw error inherent deviation of the wind turbine generator set based on the power curve analysis as claimed in claim 1, wherein in the step 3), the yaw error inherent deviation analysis data set { X }iThe interval division method comprises the following steps:
3-a) plotting yaw error θiThe frequency distribution histogram of the yaw error range to be analyzed is set, and the lower limit theta of the yaw error range to be analyzed is set based on the distribution condition of the frequency distribution histogramlbAnd an upper bound θub;
3-b) set yaw error intrinsic bias analysis dataset { XiDividing the interval of the M points into M number;
4. The method for identifying and compensating the yaw error inherent deviation of the wind turbine generator based on the power curve analysis as claimed in claim 1, wherein in the step 4), the flow of obtaining the true power curve of the wind turbine generator under M yaw error intervals is as follows:
4-a) setting a real power curve to obtain an initial interval k equal to 1;
4-b) determining yaw error intrinsic bias analysis data set under k yaw error intervalCorrected wind speed inCorresponding maximum valueNote the bookWherein v iscut_offCutting out wind speed for the wind turbine generator;
4-c) dividing intervals by taking a fixed wind speed interval delta v as a wind speed interval, and analyzing a data set of yaw error inherent deviation under the k-th yaw error intervalFurther based on corrected wind speedDividing the data into a yaw error inherent deviation analysis data set in the jth corrected wind speed intervalIs defined as
k=1,2,3,…,M j=1,2,3,…,Mkm=1,2,3,…,Mk,j
Wherein M isk,jAnalyzing the data set for the yaw error inherent deviation in the jth corrected wind speed intervalThe number of data in (1); mkAnalyzing the data set for the yaw error inherent deviation under the k yaw error intervalThe number of the corrected wind speed interval divisions is calculated as follows
4-d) calculating the yaw error inherent bias analysis dataset for each corrected wind speed intervalAverage corrected wind speed ofAnd average active powerThe formula is as follows
4-e) average corrected wind speed for each corrected wind speed intervalAnd average active powerCarrying out maximum value-minimum value normalization processing to obtain normalized average corrected wind speedAnd average active power
4-f) correcting the wind speed based on the averageAnd average active powerDetermining the power curve fitting central point under each corrected wind speed intervalThe determination is as follows: analyzing the data set of the yaw error inherent deviation in the jth corrected wind speed intervalNumber of data in (M)k,jIf the power curve is equal to 0, the power curve is not fitted to the central point in the interval; inverse directionThen, the power curve in the interval is considered as the fitting center point
4-g) supplementary definition of center pointAnd recording the number of the fitting center points of the power curve in the kth yaw error interval as M'kCalculating the fitted center point of each power curveCorresponding parameter valueIs given by the formula
WhereinFitting center points for two adjacent power curvesAndcorresponding to the chord length after coordinate normalization, i.e.
dkFitting all power curves with the total chord length normalized by the coordinates corresponding to the center point, i.e.
4-h) fitting the power curve in the kth yaw error interval by adopting a least square B spline fitting algorithm, wherein a fitting function B is obtainedk(t) is defined as follows:
wherein N isn,p(t) is a standard function of the nth segment of B spline fitting function with the order p, t is an independent variable of the least square B spline fitting function,fitting the nth control point of the function to the least squares B-spline;for a segment node, i ═ 0, 1, 2, …, p-1, p, p +1, …, M'k-1,M′k,M′k+1,…,M′k+ p, the calculation is as follows:
4-i) determining a B-spline fitting function B based on a least squares optimization functionkAll control points in (t)
4-j) solving the least square B-spline fitting function Bk(t) as a result of the true power curve in the kth yaw error interval { PC) converted to a polynomial form with the independent variable as the wind speed vk};
4-k) setting an interval k to be analyzed as k +1, and repeating the steps 4-b) to 4-j) until j is larger than M.
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